Royal Street Names in Brisbane City

Have you ever noticed the prevalence of Royal street names within Australia?

Living in Brisbane CBD, it’s certainly quite known that female Royal street names run in one direction and males the other.

We can quite easily observe the commonality of this across Brisbane …

Project setup

# install.packages("sf")
# install.packages("rmapshaper")
# install.packages("leaflet")
# install.packages("rgeos")
# install.packages("plotly")

# Restore our r environment.
renv::restore(prompt = F)

# Load required packages.
library(sf)             # For reading Shape files.
library(rgeos)          # For reducing Shape files.
library(leaflet)        # Plotting Shape files.
library(data.table)     # For data.tables.
library(plotly)         # For plotsY.

# Define a regex pattern of word-bounded Royal female and males names.
royal_female <- "\\b(Adelaide|Cordelia|countess|Cordeilla|Ann|Queen|Victoria|Mary|Louise|Alexandra|Elizabeth|Alice|Margaret|Charlotte|Augusta|Helena|Maud|Agnes|Helene|Caroline|Olga|Louisa|Diana)\\b"
royal_male <- "\\b(Albert|George|Charles|Edward|earl|Christian|Frederick|Louis|Arthur|William|Henry|Alexander|John|Philip|Douglas|Ernest|Patrick|Friederike|Auguste|Francis|David|Augustus|Antony)\\b"

regex_holden   <- "statesman|holden|calais|commodore|kingswood"
regex_art      <- "Angelico|Gainsborough|Picasso|Michelangelo|Botticelli|Rubens|Van Dyc|Vermeer|Matisse|Raphael|Drysdale|Delacroix|Rembrand|michelangel|Ruisdael|da vinci|van go|\\bhart\\b"
regex_space    <- "\\(bursa|balsa)\\b|andromeda|taurus|capricorn|crater|\\blibra\\b|Voyager|shuttle|galaxy|gemini|constell|lunar|asteroid|saturn|eclipse|halleys|apollo|Capella|orbit"
regex_trees    <- "cypress|poplar|yulan|blackthorn|japonica|rosemary|berrigan|lavender|magnolia|poinciana|frangipani|jarah|hydrangea|zalea|iris\\b|viloet|orchid|applegum|silky *oak|ironbark|mahogany|arbour|fiddlewood|bottlebrush|Wisteria|coachwood|sandalwood|wattle|waratah|cedar|fig tree|eucalypt|river.*gum|\\boak\\b"
regex_wine     <- "Champagne|Marsala|Claret|Frontignac|Sherry|Burgundy|Cabernet|Riesling|Tintara|Yaldara"
regex_birds    <- "kingfisher|quail|swallow|seagul|resella|nightingale|pelican|cockatoo|robin|lorikeet|fantail|\\bdove\\b|lyrebird|lotusbird|madder|orangetip|shelduck|\\bswan\\b"
regex_celebs   <- "Brando|Jagger|hoffman|macnee|dicaprio|monroe|van dyke|streisand|culkin|cosby|hanks|minnelli|pfeiffer|de vito|thurman"
regex_ordinals <- "first|second|third|fourth|fifth|sixth|seventh"
regex_first_f  <- "cook|daniel|carl|whitby|eagle|solander|pembroke|endeavour|botany|walker|banks|hodges|magdalena|carl"

if(F){
    # Read the shapefile
    shapefile_orig <- st_read("C:\\temp\\QSC_Extracted_Data\\QSC_Extracted_Data_20240418_144922083000-53700\\Queensland_roads_and_tracks.shp")

    themes <- shapefile_orig[
        shapefile_orig$ROAD_NAME_ %ilike% paste(
            regex_holden, 
            regex_art, 
            regex_space, 
            regex_trees, 
            regex_wine,
            regex_birds,
            regex_celebs,
            regex_ordinals,
            regex_first_f,
            sep = "|"),]

    st_write(themes, "./inputs/themes.shp")

    # We're only interested in Brisbane City and of the below road class types.
    shapefile <- shapefile_orig[
        shapefile$LGA_NAME_R == "Brisbane City"
    , ]
    shapefile <- shapefile[shapefile$ROAD_NAME_ %ilike% royal_female | shapefile$ROAD_NAME_ %ilike% royal_male, ]

    dt <- data.table(shapefile)

    View(dt[, .N, ROAD_NAME_][order(-N)])

    remove <- "Saint Helena|Mary Ellen| HOLT |CHRISTIAN COL|M MAC|OUTREACH|E ANNE L|FARRIOR|WILLIAM FAR| LESLIE |TONGE|JOHN FRAN|JOHN FINN|SAINT GEOR|JOHN FISH| GOD |SAINT|BISHOP|JOHN BRI|ISLEY| ULM |ARTHUR GO|SIR DA|GASKIN|SIRETT|JOLLY|COOKMAN|E PAGE"

    shapefile <- shapefile[!shapefile$ROAD_NAME_ %ilike% remove, ]

    dt <- data.table(shapefile)

    View(dt[, .N, ROAD_NAME_][order(-N)])

    st_write(shapefile, "./inputs/queens_n_queens.shp")

    head(shapefile)
    }

# Define the input path to our shapefile.
shapefile <- st_read("./inputs/queens_n_queens.shp")
 
# Create a colour column that allows us to differential between female and male.
shapefile$color <- fifelse(shapefile$ROAD_NAME_ %ilike% royal_female, "red", "blue")
# And gender
shapefile$gender <- fifelse(shapefile$ROAD_NAME_ %ilike% royal_female, "Female", "Male")

# Coerce to a data.table.
dt <- data.table(shapefile)

# Inspect frequencies.
dt[, .N, ROAD_NAME_][order(-N)]


# Create a leaflet map.
    # - We'll use a minimilist base map.
    # - Add the polylines from the shapefile and allow for street name popups.
    # - Add coloured lines.
map <-  leaflet() %>%
    addProviderTiles(providers$CartoDB.Positron) %>%
    addPolylines(
        data = shapefile,
        color = ~color,
        weight = 3,
        popup = ~ROAD_NAME_
    ) %>%
    addLegend(
        colors = c("red", "blue"),
        labels = c("Female names", "Male names"),
        position = "bottomright"
    )

# Display the map.
map

We can zoom in to the CBD to highlight the directions by gender:

map %>%
  setView(lng = "153.0255184", lat = "-27.4687535", zoom = 15)

We can also calculate the lengths of each of the streets …

# Calculate length/distance.
dt[, street_length := st_length(shapefile)]

# Inspect.
dt[, .(ROAD_NAME_, street_length)]

           ROAD_NAME_ street_length
               <char>       <units>
  1:  Adelaide Street  28.33155 [m]
  2:     Queen Street 145.81836 [m]
  3:    Arthur Street 308.68295 [m]
  4:     Alice Street  92.87580 [m]
  5: Alexandra Street 104.41105 [m]
 ---                               
781:     Alice Street 217.05599 [m]
782:    Albert Street  99.27984 [m]
783:     Albert Place 145.69407 [m]
784: Alexandra Street  88.38151 [m]
785:   Arthur Terrace  74.34477 [m]

# Calculate cumulative length by gender.
gen <- dt[, .(cumulative_length = sum(street_length)), by = gender]

# Plot the cumulative lengths for both genders.
plot_ly(gen, x = ~gender, y = ~cumulative_length, type = 'bar', marker = list(color = c("pink", "#ADD8E6"))) %>%
  layout(title = "Distance by Gender",
         xaxis = list(title = "Gender"),
         yaxis = list(title = "Distance"))

Themed Streets

# Import themed streets.
shapefile <- st_read("./inputs/themes.shp")

Reading layer `themes' from data source 
  `C:\Users\kyleh\GitHub\KyleHaynes.github.io\inputs\themes.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 10005 features and 48 fields
Geometry type: LINESTRING
Dimension:     XY
Bounding box:  xmin: 138.5856 ymin: -28.85879 xmax: 153.5363 ymax: -9.952433
Geodetic CRS:  GDA94

# Colour them.
# Create a named vector.
vec <- c(
    "blue" = "Holden",
    "orange" = "Artists",
    "purple" = "Space",
    "#0db7dc" = "Wine Region",
    "green" = "Flora",
    "#1abc9c" = "Birds",
    "#22847a" = "Celebs",
    "black" = "Ordinals"
)

shapefile$color <- fcase(
    shapefile$ROAD_NAME_ %ilike% regex_holden,  names(vec)[1],
    shapefile$ROAD_NAME_ %ilike% regex_art,     names(vec)[2],
    shapefile$ROAD_NAME_ %ilike% regex_space,   names(vec)[3],
    shapefile$ROAD_NAME_ %ilike% regex_wine,    names(vec)[4],
    shapefile$ROAD_NAME_ %ilike% regex_trees,   names(vec)[5],
    shapefile$ROAD_NAME_ %ilike% regex_birds,   names(vec)[6],
    shapefile$ROAD_NAME_ %ilike% regex_celebs,  names(vec)[7],
    shapefile$ROAD_NAME_ %ilike% regex_first_f, names(vec)[8]
)


# And gender

map <- leaflet() %>%
    addProviderTiles(providers$CartoDB.Positron) %>%
    addPolylines(
        data = shapefile[],
        color = ~color,
        weight = 3,
        popup = ~ROAD_NAME_
    ) %>%
    addLegend(
        colors = names(vec),
        labels = vec,
        position = "bottomright"
    )

Warning: sf layer has inconsistent datum (+proj=longlat +ellps=GRS80 +no_defs).
Need '+proj=longlat +datum=WGS84'

### Holden Streets: Sunnybank Hills

map %>%
  setView(lng = "153.0411963", lat = "-27.5953411", zoom = 17)

### Artists & Trees: Mackenzie

map %>%
  setView(lng = "153.1252209", lat = "-27.5418537", zoom = 16)

### Wine Region: Carseldine

map %>%
  setView(lng = "153.0068839", lat = "-27.3517528", zoom = 16)